The paper introduces SC3, a user-friendly tool for unsupervised clustering of single-cell RNA-seq (scRNA-seq) data. SC3 combines multiple clustering solutions through a consensus approach to achieve high accuracy and robustness. The authors demonstrate that SC3 can identify subclones based on transcriptomes from neoplastic cells collected from patients. SC3 is benchmarked against five other methods, showing superior performance in terms of accuracy and stability. The tool is available as an R package and can be integrated into existing bioinformatic workflows. The paper also discusses the impact of various parameters on the clustering results and provides methods for identifying differentially expressed genes, marker genes, and outlier cells. SC3 is particularly useful for identifying rare cell types and subclones, as demonstrated in the analysis of myeloproliferative neoplasms.The paper introduces SC3, a user-friendly tool for unsupervised clustering of single-cell RNA-seq (scRNA-seq) data. SC3 combines multiple clustering solutions through a consensus approach to achieve high accuracy and robustness. The authors demonstrate that SC3 can identify subclones based on transcriptomes from neoplastic cells collected from patients. SC3 is benchmarked against five other methods, showing superior performance in terms of accuracy and stability. The tool is available as an R package and can be integrated into existing bioinformatic workflows. The paper also discusses the impact of various parameters on the clustering results and provides methods for identifying differentially expressed genes, marker genes, and outlier cells. SC3 is particularly useful for identifying rare cell types and subclones, as demonstrated in the analysis of myeloproliferative neoplasms.